通过不确定性感知的测试时自适应，提升肺部CT图像配准的逆一致性与精度。
Uncertainty-Aware Test-Time Adaptation for Inverse Consistent Diffeomorphic Lung Image Registration
- 用MC dropout估计空间不确定性，指导测试时模型优化
- 在675例数据上实现0.966的肺部边界Dice系数，优于基线方法
- 特别适合需要高精度逆一致性的医学图像配准场景
微分同胚形变图像配准确保吸气与呼气胸腔CT扫描间变换的平滑可逆性。然而，基于深度学习的微分同胚方法在捕捉大形变时仍难以保持逆一致性，且未考虑模型不确定性。本文提出一种不确定性感知的测试时自适应框架，用于实现逆一致的微分同胚肺部图像配准。通过蒙特卡洛丢弃法估计空间不确定性，并用于改进模型性能。在来自COPDGene研究的675名受试者的大规模数据集上进行吸气到呼气的CT配准训练与评估，本方法在肺部边界上的骰子相似系数（DSC）达到0.966，高于VoxelMorph（0.953）和TransMorph（0.953）。在反向配准中也表现一致提升，整体DSC为0.966，优于VoxelMorph（0.958）和TransMorph（0.956）。配对t检验表明差异具有统计显著性。
原文摘要 · Abstract (English)
Diffeomorphic deformable image registration ensures smooth invertible transformations across inspiratory and expiratory chest CT scans. Yet, in practice, deep learning-based diffeomorphic methods struggle to capture large deformations between inspiratory and expiratory volumes, and therefore lack inverse consistency. Existing methods also fail to account for model uncertainty, which can be useful for improving performance. We propose an uncertainty-aware test-time adaptation framework for inverse consistent diffeomorphic lung registration. Our method uses Monte Carlo (MC) dropout to estimate spatial uncertainty that is used to improve model performance. We train and evaluate our method for inspiratory-to-expiratory CT registration on a large cohort of 675 subjects from the COPDGene study, achieving a higher Dice similarity coefficient (DSC) between the lung boundaries (0.966) compared to both VoxelMorph (0.953) and TransMorph (0.953). Our method demonstrates consistent improvements in the inverse registration direction as well with an overall DSC of 0.966, higher than VoxelMorph (0.958) and TransMorph (0.956). Paired t-tests indicate statistically significant improvements.
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